We have developed a reduced representation model (RRM) to predict the folded structures of proteins from their primary sequences and random starting conformations. The molecular structure of each protein has been reduced to it backbone atoms (with ideal fixed bond lengths and valence angles) and each side chain approximated by a single virtual united-atom. The coordinate variables were the backbone dihedral angles ???$phi$ and $psi$???. A statistical potential function, that included local and non-local interactions and was computed from known protein structures, was used in the structure minimization. A novel approach, employing the concepts of Genetic Algorithms, has been developed to simultaneously optimize a population of conformations. With the information of primary sequence and the radius of gyration of the crystal structure only, and starting from randomly generated initial conformations, we have been able to fold several proteins to their native like conformation with high computational convergence. This application aims to utilize the PSC computational resource to further develop the RRM software and test running results.